Gait Recognition Datasets.
Current publicly available gait recognition datasets mainly belong to two series, i.e., the CASIA series [1]}, [2]}, [3]} and the OU-ISIR series [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]} as listed in Table REF .
The CASIA series were built in the early research of gait recognition, which facilitated the initial exploration of RGB images and silhouettes for gait representations [3]}, [12]}.
Despite its smaller number of subjects, the CASIA-B [2]} is still the most widely used dataset for the evaluation of silhouette-based methods.
The OU-ISIR series were first built ten years ago and developed comprehensive variants such as walking at different speeds [4]}, clothing styles [5]}, and bags [9]}, subjects of different ages [8]}, and annotations of 2D pose [10]}.
Due to their large population, the OU-LP [7]} and OU-MVLP [20]} also became the most popular datasets for current research.
However, the above datasets were collected in constrained scenes like labs [2]}, [7]} or a small defined area on a campus [1]}, [24]}.
Most recently, researchers started to narrow the gap between in-the-lab research and real-world application.
As a contemporaneous study of our work, Zhu et al. [25]} constructed the GREW dataset from natural videos collected in an open area.
However, there is no dataset that provides rich 3D representations for gait recognition in the wild.
Therefore, we need to build a new dataset that is collected from complex scenes and with dense 3D meshes for gait recognition in the wild.
